Conventional methods of fault diagnosis of machines such as construction machinery include the following two well known methods.
One is the method of diagnosis in which the various phenomenon items (inspection items) are treated as nodes where branching is effected in accordance with the answer (e.g. YES, NO) constituting the result of the associated inspection, so as to lead to the cause which represents the final conclusion.
Since the knowledge indicating the relationship between the various inspection items and the various cause items is expressed in a decision tree structure, this is called FTA diagnosis (Fault Tree Analysis).
In another method of diagnosis, degree of relationship data indicating the degree of relationship of the various inspection items and the various cause items are arranged in the fashion of a matrix in which either the inspection items or cause items are rows while the other one of these are the columns; frequency of occurrence data are input indicating the degree to which the prescribed inspection item of the various inspection items occurs. The likelihood of a cause item can then be calculated from these frequency of occurrence data that have been input and degree of association data, arranged in matrix fashion; the cause is inferred from these calculated certainties. Since the knowledge regarding the causal relationship between the inspection items and the fault cause items is represented in the form of a matrix, this is called matrix fuzzy diagnosis.
Furthermore, a technique whereby knowledge of a decision tree structure for FTA diagnosis is converted into knowledge arranged in matrix fashion for matrix fuzzy diagnosis is disclosed in Japanese Patent Publication H. 3-116330 and so is already public knowledge.
Also, methods of inference called incident base inference (ID3 etc.) are widely known, in which an efficient method of categorization resulting from extraction of data characteristics from incident data is represented in the form of a decision tree.
This incident base inference is a technique whereby general rules are compiled from a collection of past incidents (problem and solution set) and when a new incident is presented the solution is found by using these rules; this is utilized as one method of knowledge acquisition.
In incident base inference, a decision tree is compiled whereby classes (categorized item: is a melon, is an apple, etc.) are categorized using for example the properties of the collection of past incidents (question item: what color, what size, etc.) and property values (values that the reply to the question may take: green, red, or large, small, etc.).
A characteristic advantage of matrix fuzzy diagnosis is that the candidate fault causes can be narrowed down even if the inspection item frequency of occurrence is unanswerable or even if inspection results are input in which the frequency of occurrence is expressed in terms of uncertainty with a numerical value in the range 0 to 1.
However, there was the drawback that if the information provided by the inspection items is insufficient the precision with which the causes are narrowed down was poor.
Also, there was the problem that, although the cause of the fault is output represented by a certainty, it was not possible to present effective inspection items that would further narrow down the cause from among a plurality of candidate fault causes for which the same certainty is expressed.
Furthermore, when the inspections are to be carried out, the inspection items are simply displayed as a list, so it was not possible to ascertain which inspection, of the plurality of inspection items, would be effective for diagnosis and so the progress of the diagnosis as a whole cannot be forecast.
In contrast, a characteristic of FTA diagnosis is that this is a method wherein, as inspection proceeds from the uppermost inspection items (nodes) of the decision tree, the answer to the inspection indicates a branch which when followed successively presents the next inspection item (node) as response, so there is the advantage that the fault cause (conclusion) can be narrowed down finally to a single cause by a minimum of inspection items. A further advantage is that, since the knowledge of the FTA diagnosis is represented in the form of a decision tree, the outlook for the diagnosis as whole can easily be forecast.
However, in some cases, if an intermediate inspection item cannot be answered, or if the answer includes uncertainty, a final solution of the diagnosis cannot be obtained.
Thus, as mentioned above, matrix fuzzy diagnosis was subject to the problems regarding accuracy and operational efficiency that:
Problem 1: if there are many inspection items, it was difficult to keep all the inspection items in view;
Problem 2: one cannot decide which inspection item is the more necessary at a particular time-point: in other words, unnecessary inspection items cannot be excluded; and
Problem 3: if answers (causes) having the same certainty are obtained, one cannot tell which inspection items would be effective to narrow these causes down further.
Also, FTA diagnosis was subject to the problems regarding accuracy and operational efficiency that:
Problem 4: since answers to inspection items are represented by branches, only answers possessing no uncertainty (e.g. YES, NO etc.) can be used i.e. information including uncertainty cannot be handled;
Problem 5: since the order of the inspections is fixed, it was not possible to prioritize use of already-known information; also, if an inspection item is unclear the diagnosis was held up; and
Problem 6: until all the inspection items relating to a given cause item have been answered, it was not even possible to see the trend of the diagnostic results.
Furthermore, FTA diagnosis was subject to the problem regarding operational efficiency that in some cases knowledge for diagnosis of causes is prepared using a plurality of decision tree structures, and in such cases:
Problem 7: even if the knowledge is arranged in decision tree structure, since there is a plurality of decision trees, inspection items and cause items can be duplicated, making it hard to obtain an overall grasp of the FTA knowledge. In other words, whichever decision tree is examined it is hard to tell what diagnosis should be made.